Organizations today are continually increasing their use of predictive analytics to more accurately predict their business outcomes, to improve business performance, and to increase profitability. Common and yet also highly strategic predictive modeling applications include fraud detection, rate making, credit scoring, customer retention, customer lifetime value, customer attrition/churn, and marketing response models.
As the sheer number of these models increases to support more and more business objectives, so does the requirement to manage these models reliably and securely as valuable corporate assets. Many companies, especially those in the financial services sector, also need to demonstrate adherence to model validation and growing external compliance demands (e.g., governance practices outlined by the Office of the Comptroller of the Currency Administrator of National Banks (2000), the Basel II Committee's Accord Implementation Group (Basel Committee on Banking Supervision 2005), and other governing bodies). The difficulty in handling predictive models can be further exacerbated because of the greater quantity of data to be analyzed and the need to have many different types of models available to determine which models should be used to predict which portions of that large quantity of data.